1. Field of the Invention
The present invention relates to data processing apparatuses, data processing methods, and media, and more particularly, to a data processing apparatus, a data processing method, and a medium which allow an image having a low signal-to-noise (s/n) ratio to be converted to an image having a high s/n ratio.
2. Description of the Related Art
The assignee of the present invention has already proposed class-classification adaptive processing as processing for improving the quality of an image, such as an s/n ratio and a resolution, and for improving an image.
The class-classification adaptive processing is formed of class-classification processing and adaptive processing. The class-classification processing classifies data into classes according to its characteristic and the adaptive processing is applied to each class.
In the adaptive processing, pixels (hereinafter called low-s/n pixels, if necessary) constituting a low-s/n image (an image to be processed by the class-classification adaptive processing) are linearly coupled with predetermined prediction coefficients to obtain the prediction values of the pixels of the original image, which is a high-s/n image, of the low-s/n image. With the adaptive processing, an image is obtained by removing noise from the low-s/n image, or an image is obtained by improving blur in the low-s/n image.
More specifically, for example, it is assumed that the original image (such as an image having no noise or image having no blur) is used as master data, a low-s/n image obtained by superposing noise on the original image or by adding blur thereto is used as apprentice data, and the prediction value E[y] of the pixel value xe2x80x9cyxe2x80x9d of a pixel (hereinafter called the original pixel, if necessary) constituting the original image is to be obtained by a linear coupling model specified by a linear coupling of a set of the pixel values x1, x2, . . . of several low-s/n pixels (pixels constituting the low-s/n image) and predetermined prediction coefficients w1, w2, . . . In this case, the prediction value E[y] is expressed by the following expression.
E[y]=w1x1+w2x2+xe2x80x83xe2x80x83(1)
To generalize the expression (1), a matrix xe2x80x9cWxe2x80x9d formed of the set of the prediction coefficients xe2x80x9cwxe2x80x9d, a matrix xe2x80x9cXxe2x80x9d formed of the set of the apprentice data, and a matrix xe2x80x9cYxe2x80x2xe2x80x9d formed of the prediction values E[y] are defined in the following way.       X    =          xe2x80x83        ⁢          [                                                  X              11                                                          X              12                                            …                                              X                              1                ⁢                n                                                                                        X              21                                                          X              22                                            …                                              X                              2                ⁢                n                                                                          …                                …                                …                                …                                                              X              m1                                                          X              m2                                            …                                              X              mn                                          ⁢              xe2x80x83            ]                  W      =              [                  xe2x80x83                ⁢                                                            W                1                                                                                        W                2                                                                        …                                                                          W                n                                                    ⁢                  xe2x80x83                ]              ,          xe2x80x83        ⁢                  Y        xe2x80x2            =              [                  xe2x80x83                ⁢                                                            E                ⁡                                  [                                      y                    1                                    ]                                                                                                        E                ⁡                                  [                                      y                    2                                    ]                                                                                        …                                                                          E                ⁡                                  [                                      y                    m                                    ]                                                                    ⁢                  xe2x80x83                ]            
Then the following observation equation is derived.
XW=Yxe2x80x2xe2x80x83xe2x80x83(2)
A component xij of the matrix X indicates the j-th apprentice data in the i-th apprentice-data set (apprentice-data set used for predicting the i-th master data yi), and a component wj in the matrix W indicates a prediction coefficient to be multiplied by the J-th apprentice data in the apprentice-data set. The i-th master data is indicated by yi, and therefore, E[yi] indicates the prediction value of the i-th master data.
It is also assumed that the least squares method is applied to this observation equation to obtain a prediction value E[y] close to the pixel value xe2x80x9cyxe2x80x9d of the original pixel. In this case, when a matrix xe2x80x9cYxe2x80x9d formed of the set of the true pixel values xe2x80x9cyxe2x80x9d (true values) of the original pixels serving as master data and a matrix xe2x80x9cExe2x80x9d formed of the set of the remainders xe2x80x9cexe2x80x9d of the prediction values E[y] against the pixel values xe2x80x9cyxe2x80x9d of the original pixels are defined in the following way,       E    =          [                                                  e              1                                                                          e              2                                                            …                                                              e              m                                          ⁢              xe2x80x83            ]        ,      xe2x80x83    ⁢      Y    =          [              xe2x80x83            ⁢                                                  y              1                                                                          y              2                                                            …                                                              y              m                                          ⁢              xe2x80x83            ]      
the following remainder equation is derived, from the equation (2).
XW=Y+Exe2x80x83xe2x80x83(3)
In this case, the prediction coefficient wi used for obtaining the prediction value E[y] close to the pixel value xe2x80x9cyxe2x80x9d of the original pixel is obtained by setting the square error,       ∑          i      =      1        m    ⁢      xe2x80x83    ⁢      e    i    2  
to the minimum.
Therefore, the prediction coefficient wi obtained when the above square error differentiated by the prediction coefficient wi is zero, in other words, the prediction coefficient wi satisfying the following expression, is the most appropriate value for a prediction value E[y] close to the pixel value xe2x80x9cyxe2x80x9d of the original pixel.                                                         e              1                        ⁢                                          ∂                                  e                  1                                                            ∂                                  w                  i                                                              +                                    e              2                        ⁢                                          ∂                                  e                  2                                                            ∂                                  w                  i                                                              +          …          +                                    e              m                        ⁢                                          ∂                                  e                  m                                                            ∂                                  w                  i                                                                    =                  0          ⁢                      xe2x80x83                    ⁢                      (                                          i                =                1                            ,              2              ,              …              ⁢                              xe2x80x83                            ,              n                        )                                              (        4        )            
The expression (3) is differentiated by the prediction coefficient wi to obtain the following expressions.                                                         ∂                              e                i                                                    ∂                              w                1                                              =                      x            i1                          ,                                            ∂                              e                i                                                    ∂                              w                2                                              =                      x            i2                          ,        …        ⁢                  xe2x80x83                ,                                            ∂                              e                i                                                    ∂                              w                n                                              =                      x            in                          ,                  xe2x80x83                ⁢                  (                                    i              =              1                        ,            2            ,            …            ⁢                          xe2x80x83                        ,            m                    )                                    (        5        )            
From the expressions (4) and (5), the expression (6) is derived.                                                         ∑                              i                =                1                            m                        ⁢                          xe2x80x83                        ⁢                                          e                i                            ⁢                              x                i1                                              =          0                ,                                            ∑                              i                =                1                            m                        ⁢                          xe2x80x83                        ⁢                                          e                i                            ⁢                              x                i2                                              =          0                ,                              …            ⁢                          xe2x80x83                        ⁢                                          ∑                                  i                  =                  1                                m                            ⁢                              xe2x80x83                            ⁢                                                e                  i                                ⁢                                  x                  in                                                              =          0                                    (        6        )            
With the relationship among the apprentice data xe2x80x9cxxe2x80x9d, the prediction coefficients xe2x80x9cwxe2x80x9d, the master data xe2x80x9cyxe2x80x9d, and the remainders xe2x80x9cexe2x80x9d in the remaining equation (3) being taken into account, the following normal equations are obtained from the expression (6).                     {                                                                                                                        (                                                                        ∑                                                      i                            =                            1                                                    m                                                ⁢                                                  xe2x80x83                                                ⁢                                                                              x                            i1                                                    ⁢                                                      x                            i1                                                                                              )                                        ⁢                                          w                      1                                                        +                                                            (                                                                        ∑                                                      i                            =                            1                                                    m                                                ⁢                                                  xe2x80x83                                                ⁢                                                                              x                            i1                                                    ⁢                                                      x                            i2                                                                                              )                                        ⁢                                          w                      2                                                        +                  …                  +                                                            (                                                                        ∑                                                      i                            =                            1                                                    m                                                ⁢                                                  xe2x80x83                                                ⁢                                                                              x                            i1                                                    ⁢                                                      x                            in                                                                                              )                                        ⁢                                          w                      n                                                                      =                                  (                                                            ∑                                              i                        =                        1                                            m                                        ⁢                                          xe2x80x83                                        ⁢                                                                  x                        i1                                            ⁢                                              y                        i                                                                              )                                                                                                                                                                    (                                                                        ∑                                                      i                            =                            1                                                    m                                                ⁢                                                  xe2x80x83                                                ⁢                                                                              x                            i2                                                    ⁢                                                      x                            i1                                                                                              )                                        ⁢                                          w                      1                                                        +                                                            (                                                                        ∑                                                      i                            =                            1                                                    m                                                ⁢                                                  xe2x80x83                                                ⁢                                                                              x                            i2                                                    ⁢                                                      x                            i2                                                                                              )                                        ⁢                                          w                      2                                                        +                  …                  +                                                            (                                                                        ∑                                                      i                            =                            1                                                    m                                                ⁢                                                  xe2x80x83                                                ⁢                                                                              x                            i2                                                    ⁢                                                      x                            in                                                                                              )                                        ⁢                                          w                      n                                                                      =                                  (                                                            ∑                                              i                        =                        1                                            m                                        ⁢                                          xe2x80x83                                        ⁢                                                                  x                        i2                                            ⁢                                              y                        i                                                                              )                                                                                        …                                                                                                                                      (                                                                        ∑                                                      i                            =                            1                                                    m                                                ⁢                                                  xe2x80x83                                                ⁢                                                                              x                            in                                                    ⁢                                                      x                            i1                                                                                              )                                        ⁢                                          w                      1                                                        +                                                            (                                                                        ∑                                                      i                            =                            1                                                    m                                                ⁢                                                  xe2x80x83                                                ⁢                                                                              x                            in                                                    ⁢                                                      x                            i2                                                                                              )                                        ⁢                                          w                      2                                                        +                  …                  +                                                            (                                                                        ∑                                                      i                            =                            1                                                    m                                                ⁢                                                  xe2x80x83                                                ⁢                                                                              x                            in                                                    ⁢                                                      x                            in                                                                                              )                                        ⁢                                          w                      n                                                                      =                                  (                                                            ∑                                              i                        =                        1                                            m                                        ⁢                                          xe2x80x83                                        ⁢                                                                  x                        in                                            ⁢                                              y                        i                                                                              )                                                                                        (        7        )            
The same number of normal equations (7) as that of prediction coefficients xe2x80x9cwxe2x80x9d to be obtained can be generated when a certain number of apprentice data xe2x80x9cxxe2x80x9d and master data xe2x80x9cyxe2x80x9d are prepared. Therefore, the equations (7) are solved (to solve the equations (7), it is necessary that the matrix formed of the coefficients applied to the prediction coefficients xe2x80x9cwxe2x80x9d be regular) to obtain the most appropriate prediction coefficients xe2x80x9cwxe2x80x9d. It is possible to use a sweeping method (Gauss-Jordan elimination method) to solve the equations (7).
As described above, the most appropriate prediction coefficients xe2x80x9cwxe2x80x9d are obtained first, and then, a prediction value E[y] close to the pixel value xe2x80x9cyxe2x80x9d of the original pixel is obtained from the expression (1) by the use of the prediction coefficients xe2x80x9cw.xe2x80x9d This is the adaptive processing.
The adaptive processing differs, for example, from a simple interpolation processing in that a component not included in a low-s/n image but included in the original image is reproduced. In other words, the adaptive processing is the same as interpolation processing using a so-called interpolation filter as far as the expression (1) is seen. Since the prediction coefficients xe2x80x9cw,xe2x80x9d which correspond to the tap coefficients of the interpolation filter are obtained by learning with the use of mater data xe2x80x9cy,xe2x80x9d a component included in the original image can be reproduced. This means that a high-s/n image can be easily obtained. From this condition, it can be said that the adaptive processing has an image creation (resolution improving) function. Therefore, the processing can be used not only for a case in which prediction values of the original image are obtained from a low-s/n image by removing noise and blur, but also for a case in which a low-resolution or standard-resolution image is converted to a high-resolution image.
FIG. 1 shows an example structure of an image processing apparatus for converting a low-s/n image to a high-s/n image by the above-described class-classification adaptive processing.
In FIG. 1, a frame memory 1 sequentially receives a low-s/n image to be processed, and temporarily stores the input low-s/n image, for example, in units of frames. The frame memory 1 stores a plurality of frames of the low-s/n image by bank switching. Even if a low-s/n moving image is input to the frame memory 1, the image can be input and output in real time by bank switching.
A class-tap generation circuit 2 sets the original pixel (the original pixel does not actually exist and is estimated) for which a prediction value is to be obtained by class-classification adaptive processing, to an aimed-at original pixel; extracts low-s/n pixels used for class classification for the aimed-at original image from the low-s/n image, stored in the frame memory 1; and outputs the extracted low-s/n pixels to a class classification circuit 4 as a class tap. More specifically, the class-tap generation circuit 2 reads some low-s/n pixels close to the aimed-at original pixel in a spatial or time manner, and outputs them to the class classification circuit 4 as a class tap.
The class classification circuit 4 classifies the aimed-at original pixel into a class according to the class tap sent from the class-tap generation circuit 2, and sends the class code corresponding to the class obtained as a result to a coefficient RAM 5 as an address. More specifically, the class classification circuit 4 applies, for example, a one-bit adaptive dynamic range coding (ADRC) to the class tap sent from the class-tap generation circuit 2, and outputs the ADRC code obtained as a result to the coefficient RAM 5 as a class code.
In K-bit ADRC processing, for example, the maximum value MAX and the minimum value MIN of the pixel values of low-s/n pixels constituting a class tap are detected; the difference therebetween, DR=MAXxe2x88x92MIN, is set to the local dynamic range of the set; and the low-s/n pixels constituting the class tap are re-quantized to K bits according to the dynamic range DR. More specifically, the minimum value MIN is subtracted from the pixel values of the pixels constituting the class tap, and the subtracted values are divided (quantized) by DR/2K. Therefore, when one-bit ADRC processing is applied to a class tap, the pixel value of each low-s/n pixel constituting the class tap is expressed by one bit. In this case, the one-bit pixel values of the pixels constituting the class tap, obtained as described above, are arranged in a predetermined order, and the arranged bit string is output as an ADRC code.
The coefficient RAM 5 stores the prediction-coefficient set of each class, obtained by learning in a learning apparatus shown in FIG. 2. When a class code is sent from the class classification circuit 4, the coefficient RAM 5 reads the prediction-coefficient set stored at the address corresponding to the class code and sends it to a prediction calculation circuit 6.
On the other hand, a prediction-tap generation circuit 3 extracts the low-s/n pixels used for obtaining the prediction value of the aimed-at original pixel in the prediction calculation circuit 6 from the low-s/n image stored in the frame memory 1, and sends the extracted low-s/n pixels to the prediction calculation circuit 6 as a prediction tap. In other words, the prediction-tap generation circuit 3 generates a prediction tap having the same structure, for example, as a class tap for the aimed-at original pixel, and outputs it to the prediction calculation circuit 6.
The prediction calculation circuit 6 uses the set of prediction coefficients w1, w2, . . . for the class of the aimed-at pixel, sent from the coefficient RAM 5, and the pixel values x1, x2, . . . of the pixels constituting the prediction tap sent from the prediction-tap generation circuit 3 to calculate the expression (1) to obtain the prediction value E[y] of the aimed-at original pixel xe2x80x9cy,xe2x80x9d and outputs the prediction value E[y] as the pixel value of a high-s/n pixel which is improved in s/n from a low-s/n pixel.
FIG. 2 shows an example structure of a learning apparatus for obtaining the prediction-coefficient set of each class, to be stored in the coefficient RAM 5.
The original image (for example, a high-s/n moving image) serving as master data is sent to the learning apparatus in units of frames. The original image is sequentially stored in a frame memory 11 having the same structure as the frame memory 1 shown in FIG. 1. The high-s/n image, which is the original image stored in the frame memory 11, is sent to a downconverter 12, and the downconverter 12, for example, reduces the resolution or adds noise to make a low-s/n image.
The low-s/n image obtained by the downconverter 12 is sequentially sent and stored as apprentice data by a frame memory 13 having the same structure as the frame memory 1 shown in FIG. 1.
When the low-s/n images corresponding to all the original images prepared for learning processing are stored in the frame memory 13 as described above, a class-tap generation circuit 14 and a prediction-tap generation circuit 15 read low-s/n pixels constituting a class tap and a prediction tap, respectively, of the aimed-at original pixel for which a prediction value is to be obtained, from the frame memory 13 to form the class tap and the prediction tap, respectively, in the same way as for the class-tap generation circuit 2 and the prediction-tap generation circuit 3 shown in FIG. 1. The class tap is sent to a class classification circuit 16. The prediction tap is sent to adders 17 and 19.
The class classification circuit 16 classifies the aimed at original pixel into a class by using the class tap sent from the class-tap generation circuit 14, and sends the class code obtained by the result of class classification to a prediction-tap memory 18 and to a master-data memory 20 as addresses.
The adders 17 and 19 perform the additions of prediction taps (apprentice data) and the master data.
The prediction-tap memory 18 reads the value stored at the address corresponding to the class code output from the class classification circuit 16 and sends it to the adder 17. The adder 17 uses the stored value sent from the prediction-tap memory 18 and the low-s/n pixels constituting the prediction tap, sent from the prediction-tap generation circuit 15, to calculate summation components, serving as the multiplication factors of the prediction coefficients, at the left sides of the normal equations (7). The adder 17 stores the calculation results at the address corresponding to the class code output from the class classification circuit 16 in the prediction-tap memory 18 in an overwrite manner.
The master-data memory 20 reads the value stored at the address corresponding to the class code output from the class classification circuit 16 and sends it to the adder 19. The adder 19 reads the aimed-at original image from the frame memory 11, and uses the aimed-at original pixel, the low-s/n pixels constituting the prediction tap, sent from the prediction-tap generation circuit 15, and the stored value sent from the master-data memory 20 to calculate summation components at the right sides of the normal equations (7). The adder 19 stores the calculation results at the address corresponding to the class code output from the class classification circuit 16 in the master-data memory 20 in an overwrite manner.
The adders 17 and 19 also perform multiplications shown in the equations (7). More specifically, the adder 17 executes multiplications between the low-s/n pixels xe2x80x9cxxe2x80x9d constituting the prediction tap. The adder 19 executes multiplications between the low-s/n pixels xe2x80x9cxxe2x80x9d constituting the prediction tap and the master data (aimed-at original pixel) xe2x80x9cy.xe2x80x9d
In the learning apparatus, the above-described processing is performed with all the original pixels stored in the frame memory 11 being sequentially set to aimed-at pixels.
When the above-described processing has been finished for all the original pixels, a calculation circuit 21 sequentially reads the stored values stored at the address corresponding to each class code from the prediction-tap memory 18 and the master-data memory 20, generates the normal equations (7), and solve the equations to obtain the prediction-coefficient set of each class.
It may be possible in the above-described prediction-coefficient learning processing that the number of normal equations required for obtaining prediction coefficients cannot be obtained in some classes. Default prediction coefficients can be output for such classes. Alternatively, prediction coefficients may be output which are obtained by solving normal equations generated by the additions without classification in the adders 17 and 19 with all the original pixels being set to aimed-at original pixels.
FIG. 3 shows an example structure of an image transfer apparatus which uses the above-described class-classification adaptive processing.
In this image transfer apparatus, an encoder 31 encodes a high-s/n image to generate encoded data, and a decoder 41 decodes the encoded data to produce the original high-s/n image (prediction value thereof).
The encoder 31 is formed of a downconverter 32. A high-s/n image (either moving or still digital image data) to be encoded is input to the downconverter 32. The downconverter 32 applies filtering by using a low-pass filter (LPF) and other processing to the input high-s/n image to convert it to a low-s/n image. This low-s/n image is output as encoded data, and is transferred, for example, through a satellite line, the Internet, and a transfer medium 51 such as a terrestrial wave, or is recorded into a recording medium 52, such as an optical disk, a magneto-optical disk, a magnetic disk, magnetic tape, an optical card, and a semiconductor memory.
The low-s/n image, which is the encoded data transferred through the transfer medium 51 or reproduced from the recording medium 52, is sent to a decoder 41. The decoder 41 is formed of a class-classification adaptive processing circuit 42. The class-classification adaptive processing circuit 42 has the same structure as the image processing apparatus shown in FIG. 1. Therefore, the class-classification adaptive processing circuit 42 applies the same class-classification adaptive processing as in the case shown in FIG. 1 to the low-s/n image, serving as the input encoded data, to convert it to a high-s/n image, and outputs the image.
In the learning apparatus shown in FIG. 2, the class classification circuit 16 applies the ADRC processing to the class tap to classify the aimed-at original pixel, and then normal equations are formed for each class to obtain a prediction-coefficient set for each class.
In other words, the learning apparatus obtains a prediction-coefficient set which minimizes the sum of the squares of prediction errors (errors of prediction values against the original pixel value) in a prediction tap generated for the original pixel. As a result, a prediction-coefficient set which minimizes the prediction error of each original pixel classified into a class is not obtained, but a prediction-coefficient set which minimizes the sum of the prediction errors is obtained. Therefore, when a prediction value is obtained by the use of the prediction-coefficient set corresponding to the class into which a certain original pixel is classified in the image processing apparatus shown in FIG. 1, the prediction value may have a very small prediction error (or no prediction error), or the prediction value may have not a small prediction error.
Consequently, to calculate a prediction-coefficient set for each class obtained by the above-described class classification is not necessarily a good method for calculating the most appropriate prediction-coefficient sets used to convert a low-s/n image to a high-s/n image.
Accordingly, it is an object of the present invention to provide a data processing apparatus, a data processing method, and a medium which remedy the foregoing drawback.
The foregoing object is achieved in one aspect of the present invention through the provision of a data processing apparatus for obtaining a prediction coefficient used to predict first data, from second data which has a lower quality than the first data, including prediction-data extracting means for extracting prediction data used to predict the first data, from the second data; prediction-coefficient calculation means for obtaining a prediction-coefficient set used to predict the first data by using the first data and the prediction data to both of which a predetermined weight is applied; prediction-value calculation means for obtaining the prediction value of the first data by using the prediction data and the prediction-coefficient set; prediction-error calculation means for obtaining the prediction error of the prediction value against the first data; and weight changing means for changing the weight applied to the first data and to the prediction data, according to the prediction error.
The foregoing object is achieved in another aspect of the present invention through the provision of a data processing apparatus for processing second data which has a lower quality than first data to obtain the prediction value of the first data, including prediction-data extracting means for extracting prediction data used to predict the first data, from the second data; identification-information storage means for relating identification information used to identify a prediction-coefficient set used to predict the first data, to third data and for storing them; comparison-data extracting means for extracting comparison data to be compared with the third data stored in said identification-information storage means, from the second data; retrieving means for comparing the comparison data with the third data stored in said identification-information storage means to search for third data having the pattern same as or similar to the comparison data; and prediction-value calculation means for obtaining the prediction value of the first data by using the prediction data and the prediction-coefficient set identified by the identification information corresponding to the third data having the pattern same as or similar to the comparison data.
The foregoing object is achieved in still another aspect of the present invention through the provision of a data processing apparatus for processing first data to output second data, including prediction-data extracting means for extracting prediction data used to predict the first data, from the second data; prediction-coefficient storage means for storing a plurality of prediction-coefficient sets used to predict the first data; prediction-value calculation means for obtaining the prediction value of the first data by using the prediction data and a predetermined set of the plurality of prediction-coefficient sets; prediction-error calculation means for obtaining the prediction error of the prediction value against the first data; detecting means for detecting a set of the plurality of prediction-coefficient sets, which minimizes the prediction error; identification-information storage means for storing identification information used to identify the set of the plurality of prediction-coefficient sets, which minimizes the prediction error; and output means for outputting the identification information together with the second data.
The foregoing object is achieved in yet another aspect of the present invention through the provision of a data processing apparatus for processing second data which has a lower quality than first data to obtain the prediction value of the first data, including separation means for separating the second data and identification information used to identify each of a plurality of prediction-coefficient sets, in input data; prediction-data extracting means for extracting prediction data used to predict the first data, from the second data; and prediction-value calculation means for obtaining the prediction value of the first data by using the prediction data and the set of the plurality of prediction-coefficient sets, selected according to one piece of the identification information.
The foregoing object is achieved in yet still another aspect of the present invention through the provision of a data processing apparatus including a first unit for processing first data to output second data; and a second unit for processing the second data to obtain the prediction value of the first data, wherein the first unit includes first prediction-data extracting means for extracting prediction data used to predict the first data, from the second data; prediction-coefficient storage means for storing a plurality of prediction-coefficient sets used to predict the first data; first prediction-value calculation means for obtaining the prediction value of the first data by using the prediction data and a predetermined set of the plurality of prediction-coefficient sets; prediction-error calculation means for obtaining the prediction error of the prediction value against the first data; detecting means for detecting a set of the plurality of prediction-coefficient sets, which minimizes the prediction error; identification-information storage means for storing identification information used to identify the set of the plurality of prediction-coefficient sets, which minimizes the prediction error; and output means for outputting the identification information together with the second data, and the second unit includes separation means for separating the second data and identification information used to identify each of the plurality of prediction-coefficient sets, in input data; second prediction-data extracting means for extracting prediction data used to predict the first data, from the second data; and second prediction-value calculation means for obtaining the prediction value of the first data by using the prediction data and the set of the plurality of prediction-coefficient sets, selected according to the identification information.
The foregoing object is achieved in a further aspect of the present invention through the provision of a data processing method for obtaining a prediction coefficient used to predict first data, from second data which has a lower quality than the first data, including a step of extracting prediction data used to predict the first data, from the second data; a step of obtaining a prediction-coefficient set used to predict the first data by using the first data and the prediction data to both of which a predetermined weight is applied; a step of obtaining the prediction value of the first data by using the prediction data and the prediction-coefficient set; a step of obtaining the prediction error of the prediction value against the first data; and a step of changing the weight applied to the first data and to the prediction data, according to the prediction error.
The foregoing object is achieved in a still further aspect of the present invention through the provision of a data processing method for processing second data which has a lower quality than first data to obtain the prediction value of the first data, including a step of extracting prediction data used to predict the first data, from the second data; a step of relating identification information used to identify a prediction-coefficient set used to predict the first data, to third data and of storing them; a step of extracting comparison data to be compared with the stored third data, from the second data; a step of comparing the comparison data with the stored third data to search for third data having the pattern same as or similar to the comparison data; and a step of obtaining the prediction value of the first data by using the prediction data and the prediction-coefficient set identified by the identification information corresponding to the third data having the pattern same as or similar to the comparison data.
The foregoing object is achieved in a yet further aspect of the present invention through the provision of a data processing method for processing first data to output second data, including a step of extracting prediction data used to predict the first data, from the second data; a step of storing a plurality of prediction-coefficient sets used to predict the first data; a step of obtaining the prediction value of the first data by using the prediction data and a predetermined set of the plurality of prediction-coefficient sets; a step of obtaining the prediction error of the prediction value against the first data; a step of detecting a set of the plurality of prediction-coefficient sets, which minimizes the prediction error; a step of storing identification information used to identify the set of the plurality of prediction-coefficient sets, which minimizes the prediction error; and a step of outputting the identification information together with the second data.
The foregoing object is achieved in a yet still further aspect of the present invention through the provision of a data processing method for processing second data which has a lower quality than first data to obtain the prediction value of the first data, including a step of separating the second data and identification information used to identify each of a plurality of prediction-coefficient sets, in input data; a step of extracting prediction data used to predict the first data, from the second data; and a step of obtaining the prediction value of the first data by using the prediction data and the set of the plurality of prediction-coefficient sets, selected according to one piece of the identification information.
The foregoing object is achieved in an additional aspect of the present invention through the provision of a medium for storing a program which obtains a prediction coefficient used to predict first data, from second data which has a lower quality than the first data, the program including a step of extracting prediction data used to predict the first data, from the second data; a step of obtaining a prediction-coefficient set used to predict the first data by using the first data and the prediction data to both of which a predetermined weight is applied; a step of obtaining the prediction value of the first data by using the prediction data and the prediction-coefficient set; a step of obtaining the prediction error of the prediction value against the first data; and a step of changing the weight applied to the first data and to the prediction data, according to the prediction error.
The foregoing object is achieved in a still additional aspect of the present invention through the provision of a medium for storing a program which processes second data which has a lower quality than first data to obtain the prediction value of the first data, the program including a step of extracting prediction data used to predict the first data, from the second data; a step of relating identification information used to identify a prediction-coefficient set used to predict the first data, to third data and of storing them; a step of extracting comparison data to be compared with the stored third data, from the second data; a step of comparing the comparison data with the stored third data to search for third data having the pattern same as or similar to the comparison data; and a step of obtaining the prediction value of the first data by using the prediction data and the prediction-coefficient set identified by the identification information corresponding to the third data having the pattern same as or similar to the comparison data.
The foregoing object is achieved in a yet additional aspect of the present invention through the provision of a medium for storing a program which processes first data to output second data, the program including a step of extracting prediction data used to predict the first data, from the second data; a step of storing a plurality of prediction-coefficient sets used to predict the first data; a step of obtaining the prediction value of the first data by using the prediction data and a predetermined set of the plurality of prediction-coefficient sets; a step of obtaining the prediction error of the prediction value against the first data; a step of detecting a set of the plurality of prediction-coefficient sets, which minimizes the prediction error; a step of storing identification information used to identify the set of the plurality of prediction-coefficient sets, which minimizes the prediction error; and a step of outputting the identification information together with the second data.
The foregoing object is achieved in a yet still additional aspect of the present invention through the provision of a medium for storing a program which processes second data which has a lower quality than first data to obtain the prediction value of the first data, the program including a step of separating the second data and identification information used to identify each of a plurality of prediction-coefficient sets, in input data; a step of extracting prediction data used to predict the first data, from the second data; and a step of obtaining the prediction value of the first data by using the prediction data and the set of the plurality of prediction-coefficient sets, selected according to one piece of the identification information.